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Pathogen community composition and co-infection patterns in a wild 1
community of rodents 2
3
Jessica L. Abbate1,2,3, Maxime Galan1, Maria Razzauti1, Tarja Sironen4, Liina 4 Voutilainen5, Heikki Henttonen5, Patrick Gasqui6, Jean-François Cosson1,7, Nathalie 5
Charbonnel1,* 6
7
1 CBGP, INRAE, CIRAD, IRD, Montpellier SupAgro, Univ Montpellier, Montpellier, France. 8
2 UMR MIVEGEC, IRD, CNRS, Univ Montpellier, Montpellier, France. 9
3 UMR UMMISCO, IRD, UPMC, UMI, Bondy, France. 10
4 Department of Virology, Faculty of Medicine, University of Helsinki, Helsinki, Finland. 11
5 Natural Resources Institute Finland (LUKE), FI-00790 Helsinki, Finland. 12
6 INRAE Clermont-Ferrand-Theix, Unité d’Épidémiologie Animale, Route de Theix, 63122 Saint 13
Genès Champanelle, France. 14
7 UMR BIPAR INRAE-ANSES-ENVA, Maisons-Alfort, France. 15
16
* Corresponding author: 17
E-mail address : nathalie.charbonnel@inrae.fr (N. Charbonnel) 18
19
20
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2
Abstract 21
Rodents are major reservoirs of pathogens that can cause disease in humans and livestock. It is 22
therefore important to know what pathogens naturally circulate in rodent populations, and to 23
understand the factors that may influence their distribution in the wild. Here, we describe the 24
incidence and distribution patterns of a range of endemic and zoonotic pathogens circulating among 25
rodent communities in northern France. The community sample consisted of 713 rodents, including 26
11 host species from diverse habitats. Rodents were screened for virus exposure (hantaviruses, 27
cowpox virus, Lymphocytic choriomeningitis virus, Tick-borne encephalitis virus) using antibody 28
assays. Bacterial communities were characterized using 16S rRNA amplicon sequencing of splenic 29
samples. Multiple correspondence (MCA), regression and association screening (SCN) analyses 30
were used to determine the degree to which extrinsic factors contributed to pathogen community 31
structure, and to identify patterns of associations between pathogens within hosts. We found a rich 32
diversity of bacterial genera, with 36 known or suspected to be pathogenic. We revealed that host 33
species is the most important determinant of pathogen community composition, and that hosts that 34
share habitats can have very different pathogen communities. Pathogen diversity and co-infection 35
rates also vary among host species. Aggregation of pathogens responsible for zoonotic diseases 36
suggests that some rodent species may be more important for transmission risk than others. 37
Moreover we detected positive associations between several pathogens, including Bartonella, 38
Mycoplasma species, Cowpox virus (CPXV) and hantaviruses, and these patterns were generally 39
specific to particular host species. Altogether, our results suggest that host and pathogen specificity 40
is the most important driver of pathogen community structure, and that interspecific pathogen-41
pathogen associations also depend on host species. 42
43
Keywords (6max) 44
16S rRNA amplicon high throughput sequencing; Disease Ecology; Microbial Interactions; 45
Pathobiome; Rodent reservoirs; Zoonoses 46
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3
1. Introduction 47
Infectious diseases are among the most important global threats to biodiversity, wildlife and 48
human health, and are associated with potential severe socioeconomic consequences (Daszak, 49
Cunningham & Hyatt 2000; Smith, Sax & Lafferty 2006; Jones et al. 2008). Although combatting 50
these risks is a main worldwide priority, our understanding of the processes underlying disease 51
emergence still remains too limited for developing efficient prediction, prevention and management 52
strategies. In humans, the majority of emerging pathogens originate as zoonoses from animal host 53
populations in which they naturally circulate (Taylor, Latham & Woolhouse 2001; Jones et al. 54
2008). Thus, identifying the epidemiological features (e.g., prevalence, diversity, host specificity, 55
geographic distribution) of zoonotic pathogen communities in their wild hosts, and the factors that 56
influence pathogen occurrence in those communities, is as important to human health as it is to 57
understanding the fundamentals of disease ecology (Garchitorena et al. 2017). 58
Both extrinsic and intrinsic factors can contribute to the composition of natural pathogen 59
communities within and between wild animal species, populations and individuals. Extrinsic factors 60
include geographic location, climate, periodicity of epidemic cycles and abiotic features influencing 61
inter-specific transmission opportunities (e.g., (Harvell et al. 2002; Burthe et al. 2006; Poulin et al. 62
2012)). Intrinsic factors such as host species identity, sex, age, and body condition as well as 63
genetic and immunogenetic features have also been intensively studied (e.g., (Beldomenico et al. 64
2008; Streicker et al. 2010; Streicker, Fenton & Pedersen 2013; Salvador et al. 2011; Charbonnel et 65
al. 2014; Bordes et al. 2017)). Although less investigated, inter-specific ecological interactions 66
(e.g., competition, facilitation) among pathogens within animal hosts are also likely to be an 67
important intrinsic force in determining the composition of pathogen communities. Ecological 68
interactions between free-living species are well-known to play a part in the distribution, 69
abundance, and many other qualitative and quantitative features of populations; the application of 70
this basic tenant of community ecology to pathogen incidence and expression of disease has become 71
recognized as imperative for assessing both risks and potential benefits posed to human health, 72
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agriculture, wildlife, and conservation (Pedersen & Fenton 2007). Simultaneous infection by 73
multiple parasite species is ubiquitous in nature (Petney & Andrews 1998; Cox 2001; Moutailler et 74
al. 2016). Interactions among co-infecting parasites may have important consequences for disease 75
severity, transmission and community-level responses to perturbations (Jolles et al. 2008; Telfer et 76
al. 2010). Henceforth, and through the advent of sequencing technologies in particular, it is now 77
possible and essential to investigate disease emergence from a multi-host / multi-pathogen 78
perspective (Galan et al. 2016), considering the potential influence of pathogen interactions on 79
current and future disease distributions (Cattadori, Boag & Hudson 2008; Jolles et al. 2008; 80
Budischak et al. 2015; Abbate et al. 2018). 81
Rodent communities are relevant models for developing this community ecology approach to 82
disease distribution and emergence. They harbor a wide variety of pathogenic taxa (e.g., Bordes et 83
al. 2013; Pilosof et al. 2015; Koskela et al. 2016; Diagne et al. 2017) and are important reservoir 84
hosts of agents of zoonoses that have severe implications for human health. Han et al. (2015) have 85
revealed that about 10% of the 2277 extant rodent species are reservoirs of 66 agents of zoonoses, 86
including viruses, bacteria, fungi, helminths, and protozoa. They also described 79 hyper-reservoir 87
rodent species that could be infected by multiple zoonotic agents. Among the most important 88
zoonotic pathogens, we find Echinococcus spp., Schistosoma spp., Toxoplasma spp., Trypanosoma 89
spp., Yersinia pestis, Leptospirosa spp., Bartonella spp., hantaviruses (e.g. Puumala virus) and 90
arenaviruses (e.g. Lassa virus) in particular (https://www.gideononline.com/). Strong ecological 91
interactions have been shown among some of these pathogenic taxa (Telfer et al. 2010; Kreisinger 92
et al. 2015), as well as between micro- and macroparasites (eg., (Salvador et al. 2011; Ezenwa & 93
Jolles 2015)). These interactions have been shown to be dependent on the landscape (or habitat 94
features) from which hosts were sampled (Ribas Salvador et al. 2011; Guivier et al. 2014). In 95
addition, rodents share a number of habitats with humans, including urban settings, agricultural 96
lands, and forests, providing opportunities for human-rodent contact and pathogen transmission 97
(Davis, Calvet & Leirs, 2005). Describing the distribution and composition of natural pathogen 98
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communities in rodent populations, and determining the drivers behind pathogen associations, is 99
imperative for understanding the risks they may pose for public health. 100
In this study, we analyzed the pathogen communities carried by rodent communities in a rural 101
area of northern France, a region known to be endemic for several rodent-borne diseases including 102
nephropathia epidemica (Puumala orthohantavirus (Sauvage et al. 2002)) and borreliosis (Borrelia 103
sp., Razzauti et al. 2015). We investigated exposure histories (via the presence of antibodies) for 104
several viruses (hantaviruses, cowpox virus, lymphocytic choriomeningitis virus, Tick-borne 105
encephalitis virus) and current or recent exposure to bacterial pathogens (using high-throughput 16S 106
metabarcoding). We described the pathogens detected, their prevalence in the community and their 107
individual distributions among host populations. We then tested the role of extrinsic factors (e.g., 108
habitat, host species, host age) in explaining variation in pathogen distributions, and for associations 109
(non-random co-infection frequencies) between pathogens that might indicate intrinsic drivers (e.g., 110
competition, facilitation) of pathogen community composition. We expected that host species and 111
habitat would be the most important factors structuring pathogen community composition because 112
most pathogens are largely host-specific, but those sharing habitats should also share opportunities 113
for transmission (Davies & Pedersen 2008). After accounting for extrinsic factors, we expected to 114
retrieve several pathogen-pathogen associations previously identified in the literature. This included 115
i) positive associations between cowpox virus and Bartonella infections (Microtus agrestis, (Telfer 116
et al. 2010)); ii) positive associations between distinct Mycoplasma species in mammalian hosts 117
(Sykes et al. 2008; Tagawa et al. 2012; Fettweis et al. 2014; Volokhov et al. 2017)); iii) 118
associations between Bartonella and hemotropic Mycoplasma species (both positive and negative 119
associations, as well as experimental demonstration of dynamic interactions, have been described in 120
Gerbillus andersonii (Eidelman et al. 2019)). Lastly, we also expected to find previously-121
undescribed associations due to the large bacteria and rodent dataset included in our study. All these 122
associations were likely to differ between host species, as differences in host specificity are also 123
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likely to be accompanied by differences in transmission dynamics and host responses to infection 124
(Davies & Pedersen 2008; Singer 2010; Dallas, Laine & Ovaskainen 2019). 125
126
2. Materials and methods 127
2.1. Study area and host sampling 128
Rodent sampling was conducted over two years (Autumn 2010 & 2011) in rural habitats 129
surrounding two villages (Boult-aux-Bois and Briquenay) in the Ardennes region of northern 130
France (previously described in (Gotteland et al. 2014)). Sex and age (based on precise body 131
measurements and classed as ‘adult’ for sexually mature animals and ‘juvenile’ for both juveniles 132
and sexually-immature sub-adults) were recorded for each animal, a blood sample was taken for 133
serological analyses, and animals were then euthanized using isofluorane inhalation. Spleens were 134
taken and stored in RNAlater Stabilizing Solution (Invitrogen) at -20°C. Species captured from the 135
two sites included (family: Cricetidae) 195 Arvicola scherman (montane water vole), 10 Microtus 136
agrestis (field vole), 66 Microtus arvalis (common vole), 203 Myodes glareolus (bank vole); and 137
(family: Muridae) 43 Apodemus flavicollis (yellow-necked mouse), 156 Apodemus sylvaticus (wood 138
mouse), 32 Rattus norvegicus (brown rat). These seven focal host species were collected from traps 139
placed in distinct landscapes (henceforth referred to as host ‘habitats’) (Supporting Information 140
Figure S1): R. norvegicus were found uniquely on farms, Ar. scherman and Mi. arvalis were found 141
almost entirely in meadows, and the five remaining species occupied both forests and hedgerows. 142
Demographic differences between host species were observed for sex (e.g., male bias in Ap. 143
sylvaticus; Figure S2A) and age classes (e.g., relative abundance of juveniles in Mi. arvalis and Ap. 144
slyvaticus hosts; Figure S2B). Five Microtus subterraneus (European pine vole) and one each of 145
three additional host species (one cricetid and two murids) were also found in these communities, 146
but excluded from analyses due to their rarity; these rare (non-focal) hosts and their pathogens are 147
described in Supporting Materials Appendix S1. 148
149
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2.2. Detecting virus exposure and bacterial infection 150
Among the 713 rodents sampled for this study, indirect fluorescent antibody tests (IFATs; see for 151
details (Kallio-Kokko et al. 2006)) were successfully performed on 677 animals to detect 152
immunoglobulin G (IgG) specific to or cross-reacting with cowpox virus (CPXV, Orthopoxvirus), 153
Puumala or Dobrava-Belgrade virus (respectively PUUV and DOBV, Orthohantavirus, collectively 154
referred to henceforth as “hantavirus”), lymphocytic choriomeningitis virus (LCMV, 155
Mammarenavirus), and Tick-borne encephalitis virus (TBEV, Flavivirus). We refer to these 156
antiviral antibody tests as indicating a history of past exposure, but hantavirus and LCMV 157
antibodies also likely indicate continued chronic infection. In contrast, current or very recent 158
exposure to bacterial infection was tested via 16S rRNA gene amplicon sequencing of splenic 159
tissue, giving no indication of past exposure history. Funding was available to test for bacteria in 160
just half of the animals, chosen haphazardly to equally represent all host species, study sites and 161
years, resulting in successful analysis for 332 rodents (see Figure 1 for a breakdown of number of 162
individuals sampled per focal host species). For each individual animal, the DNA from splenic 163
tissue was extracted using the DNeasy Blood & Tissue kit (Qiagen) following the manufacturer 164
recommendations. Each DNA extraction was analyzed twice independently. We followed the 165
method described in Galan et al. (2016) to perform PCR amplification, indexing, pooling, 166
multiplexing, de-multiplexing, taxonomic identification using the SILVA SSU Ref NR 119 167
database as a reference (http://www.arb-silva.de/projects/ssu-ref-nr/). Briefly, DNA samples were 168
amplified by PCR using universal primers targeting the hyper variable region V4 of the 16S rRNA 169
gene (251 bp) and sequencing via Illumina MiSeq. The V4 region has been proven to have 170
reasonable taxonomic resolution at the genus level (Claesson et al. 2010). A multiplexing strategy 171
enabled the identification of bacterial genera in each individual sample (Kozich et al. 2013). Data 172
filtering was performed as described in Galan et al. (2016) to determine presence/absence of 173
bacterial infections (summarized in Figure S3). Briefly, we discarded all bacterial OTUs containing 174
fewer than 50 reads in the entire dataset and animals for which one or both individual PCR samples 175
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produced fewer than 500 reads. A bacterial OTU was considered present in an animal if the two 176
independent PCR samples were both above a threshold number of reads, defined as the greater of 177
either 0.012% of the total number of reads in the run for that OTU (i.e. filtering using the rate of 178
indexing leak) or the maximum number of reads for that OTU in any negative control sample (i.e. 179
filtering using the presence of reads in the negative controls due to contaminations) (Galan et al. 180
2016). For each OTU suspected as pathogenic, Basic Local Alignment Search Tool (BLAST) 181
searches of the most common sequences were conducted to infer species identity where possible. 182
OTUs with at least 500 reads across all animals in the dataset were considered reliably detectable, 183
allowing us to assign absent status to these OTUs in animals failing to meet the criteria for OTU 184
presence. Only OTUs for which there were at least 500 reads across all animals in the dataset (for 185
which present and absent statuses could be assigned), and where reasonable certainty of 186
pathogenicity could be established from the literature, were considered in analyses of the pathogen 187
community. 188
189
2.3. Statistical Methods 190
All statistical analyses were implemented in R version 3.2.2 (R Core Team 2015). 191
192 2.3.1. Testing for extrinsic drivers of pathogen community composition across the rodent 193
community 194
We analyzed pathogen community composition across the whole rodent community. We first 195
estimated pathogen community richness using the Shannon diversity index (alpha diversity) 196
considering bacterial OTUs and anti-viral antibodies found in each study year, study site, habitat, 197
host species, host sex and host age group. We evaluated a linear regression model using analysis of 198
deviance (‘lm’ and ‘drop1’ functions from the basic stats package) to test for significance of 199
differences in pathogen diversity due to the fixed factors listed above after first correcting for all 200
other factors in the model (marginal error tested against the F-distribution). Post-hoc comparisons 201
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and correction for multiple tests were performed using function ‘TukeyHSD’ from package stats 202
and ‘HSD.test’ from package agricolae to regroup significantly distinct factor levels. 203
We next tested for differences in pathogen community composition (beta diversity) between host 204
species, habitats, study sites, years, age groups, and sexes. We used multiple correspondence 205
analysis (MCA) to reduce variance in presence/absence of each bacterial pathogen species and anti-206
viral antibody, implemented with the function ‘MCA’ in the FactoMinR package and visualization 207
tools found in the factoextra package. This produced a down to a set of quantitative and orthogonal 208
descriptors (dimensions) describing the pathogen community composition. With each MCA 209
dimension as a continuous dependent response variable, we then evaluated linear regression models 210
using analysis of deviance with post-hoc comparisons as detailed above. 211
212
2.3.2. Detecting and testing for associations between co-circulating pathogens 213
Because we identified a large number of pathogens, the number of potential association 214
combinations to consider was excessively high, especially with regard to the relatively small 215
number of rodents sampled. We therefore decided to test the significance only of those 216
associations (i) clearly suggested by the community-wide MCA or (ii) previously described in 217
the literature: positive association between Bartonella spp. and CPXV (Telfer et al. 2010), positive 218
associations between Mycoplasma species (Sykes et al. 2008; Tagawa et al. 2012; Fettweis et al. 219
2014; Volokhov et al. 2017), and both positive (Kedem et al. 2014; Eidelman et al. 2019) and 220
negative (Cohen, Einav & Hawlena 2015) associations between Bartonella spp. and hemotropic 221
Mycoplasma species. Given the a priori assumption that associations would differ between host 222
species, we analyzed each host species separately; where evidence suggested no significant 223
differences between host species, we pooled individuals into a single analysis to gain statistical 224
power. 225
We tested the significance of each association using both association screening (SCN) analysis 226
(Vaumourin et al. 2014) and multiple logistic regression analysis (GLMs, modeling the binomial 227
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‘presence/absence’ status of each pathogen as a function of the occurrence of other pathogens). We 228
first performed SCN analysis, as this approach is among the most suitable for detecting pathogen 229
associations in cross-sectional studies (Vaumourin et al. 2014). Briefly, given the prevalence of 230
each pathogen species in the study population, SCN analysis generates a simulation-based 95% 231
confidence envelope around the expected frequency of each possible combination of concurrent 232
infection status (a total of 2NP combinations, where NP = the number of pathogen species) under the 233
null hypothesis of random pathogen associations. Observed frequencies of co-infection 234
combinations falling above or below this envelope are considered to occur more or less frequently, 235
respectively, than in 95% of the random simulations. Significance of the association is given as a p-236
value, calculated as the number of instances in which the simulated co-infection frequency differed 237
(above or below the upper or lower threshold, respectively) from the observed frequency divided by 238
the total number of simulations (Vaumourin et al. 2014). 239
The benefit of the SCN approach is a relatively high level of statistical power and the ability to 240
identify precisely which combinations of pathogens occur outside the random expectations 241
(Vaumourin et al. (2014)). However, the SCN is sensitive to heterogeneity in the data due to 242
extrinsic factors (e.g., host specificity, or structuring in space, time, age or sex), which can both 243
create and mask true associations. A multiple logistic regression (GLM) approach was thus also 244
systematically employed, as it has the benefit of explicitly taking into account potentially 245
confounding extrinsic factors. Binomial exposure (presence/absence of either bacterial infection or 246
anti-viral antibodies) to a single pathogen was set as the dependent variable with exposure to the 247
hypothetically associated pathogen(s) treated as independent variable(s) and extrinsic factors (host 248
sex, host age, study year, study site, and where appropriate, habitat) were specified as covariates 249
using function ‘glm’ in the stats package with a binomial logit link. When there was no a priori 250
assumption concerning timing of exposure (e.g. anti-viral anti-body presence is more likely to affect 251
current acute bacterial infection than the reverse), the occurrence of each pathogen implicated in a 252
given association was set as the dependent variable in reciprocal GLMs. As for the MCA 253
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11
dimensions above, statistical significance of the association was assessed after first correcting for all 254
covariates in the model using the ‘drop1’ function (-2 log likelihood ratio tests via single-term 255
deletions compared to the full model). Despite a large number of a priori hypotheses, we regarded a 256
p-value of < 0.05 as significant due to the very low number of individuals of each host species 257
sampled. Though conceivably important, we also did not have sufficient power to test for 258
interaction terms. 259
260
3. Results 261
3.1. Taxonomic identification and prevalence of pathogens 262
3.1.1. Viral exposure 263
The most abundant virus detected was CPXV, infecting 222 (32.8%) of the 677 animals tested. It 264
was detected in all focal host species. However, significant variation in prevalence was observed 265
between focal host species (highly prevalent (43-70%) in Ar. scherman, Mi. agrestis, and My. 266
glareolus; Figure 1; χ2=111.1, df=6, p<10-4). Anti-hantavirus antibodies were detected in 16 animals 267
(2.4%), and were significantly structured among host species (with exposure highest in Mi. arvalis 268
(9.7%), R. norvegicus (3.3%) and My. glareolus (3.1%); Figure 1; χ2=12.7, df=6, p=0.048). Anti-269
LCMV antibodies were detected in two Mi. arvalis individuals (Figure 1). No animals tested 270
positive for anti-TBE antibodies. 271
272
3.1.2. Bacterial pathogens 273
Out of 952 bacterial OTUs represented by at least 50 reads in the dataset, 498 were considered 274
positive in at least one animal after data filtering (presented in Supporting Information Table S1). 275
We checked manually for potential chimera that would not have been removed from the dataset by 276
the Uchime program implemented in mothur. Two OTUs (00024 & 00037) identified as Bartonella 277
with low bootstrap values (74 and 92 respectively) appeared to represent chimeric sequences 278
between the two highly amplified genera (Bartonella and Mycoplasma) in co-infected samples. 279
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Two OTUs (00009 & 00117) which were unclassified but which had a large number of reads in 280
positive animals were also found to represent chimeric sequences between the two genera, despite 281
high bootstrap values (100). Three additional chimeric Mycoplasma OTUs with under 500 reads 282
were also excluded (OTUs 00076, 00159, and 00316). Two OTUs (00002 & 00059) were found to 283
be redundant with OTUs Myco1 and Myco3, respectively, and two more (00134 & 00220) were 284
chimera between Myco OTUs. These 11 OTUs were manually removed from the database, and are 285
not included in Table S1. 286
We identified 43 OTUs belonging to bacterial genera with members known or thought to be 287
pathogenic in mammals (Table 1). After BLAST queries, we found 16 of these OTUs (representing 288
7 distinct genera) which could be considered as reliably detectable pathogens in the focal host 289
species (Figure 1). An additional 24 OTUs were considered potentially pathogenic but excluded 290
from analyses because they were only observed in rare host species, presence-absence could not be 291
reliably established due to a low total number of reads (<500 in the dataset, e.g., Borrelia spp. and 292
Leptospira spp.), inability to rule out contamination by natural sources of non-pathogenic flora 293
during dissection (e.g., Helicobacter spp., Streptococcus spp.) or by known contaminants of 294
sequencing reagents (e.g., Williamsia spp.; (Salter et al. 2014)), or because their identity to a 295
pathogenic species was uncertain due to insufficient genetic variation at the 16S rRNA locus (e.g., 296
Yersinia spp.) (Table 1). We also identified three OTUs belonging to the eukaryotic family 297
Sarcocystidae (98% sequence similarity to the coccidian parasite Sarcocystis muris); though each 298
OTU was represented by >500 reads, there are currently no data on the reliability of this method for 299
detection (Table 1). Individual infection status for each of these OTUs is given in Table S2. 300
The 16 reliably detectable pathogenic OTUs included Bartonella spp., 10 Mycoplasma spp. 301
OTUs, Rickettsia canadensis, “Candidatus Neoehrlichia mikurensis”, Orientia spp., Brevinema 302
andersonii, and Spiroplasma spp. Phylogenetic analysis including published sequences from 303
BLAST queries revealed that the 10 Mycoplasma spp. OTUs belonged to three distinct species: M. 304
haemomuris (Myco1-3,5,7-9), M. coccoides (Myco4 and Mco6), and “Candidatus M. 305
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13
ravipulmonis” (Myco10) (Figure S4). In general, these bacterial infections were present in all but 30 306
of the 332 animals tested (90.4% prevalent), and were not concentrated in any particular focal host 307
species (χ2=9.7, df=6, p=0.139). Prevalence of each pathogen in each focal host species is presented 308
in Figure 1. 309
310
311
Figure 1. Pathogen occurrence across the rodent species comm
unity.
−0.20.00.20.40.60.81.01.2
0.0
0.2
0.4
0.6
0.8
1.0
1 185
2 8
3 59
82 105
1 22
21 40
111 30
Hantavirus
- 2
- -
- 6
5 1
- -
- 1
- 1
CPX
V -
80 1
6 -
11 49
53 1
6 1
3 7
4
LCM
V -
- -
- -
2 -
- -
- -
- -
-
0 70
2 6
3 47
27 35
0 20
16 16
54 30
Bartonella
- 57
2 4
2 41
23 19
- 18
14 15
44 3
Myco1
- -
- 4
3 38
3 4
- 1
2 -
- -
Myco2
- -
- -
- -
1 1
- 1
1 3
9 27
Myco3
- -
- -
- 9
9 12
- -
- -
- -
Myco5
- -
- -
- -
- 1
- -
- -
- 16
Myco7
- -
- -
- -
- -
- 1
2 -
2 5
Myco8
- -
- -
- 9
- -
- -
- -
- -
Myco9
- -
- -
1 3
- 3
- -
- -
- -
Myco4
- -
- -
- -
- -
- -
- 2
16 -
Myco6
- 1
- -
- -
1 2
- 1
1 -
- 10
Myco10
- -
- -
- -
- -
- -
- -
- 6
Rickettsia -
2 -
- -
4 -
- -
- -
- -
2
Neoehrlichia
- 2
- -
- v
1 3
- -
- -
2 -
Orientia
- 2
- -
- 6
- -
- -
- -
- -
Brevinem
a -
- -
- -
- 2
- -
- -
- -
-
Spiroplasma
- -
- -
- -
1 1
- -
- -
- -
Mycoplasm
a haemom
uris
Viruses
(antibodies)
Mycoplasm
a coccoides
Candidatus M
. ravipulmonis
Meadow
Farm
Landscape of trapping location
Prevalence 90%
<1%
20%
50%
Cricetidae hosts
Muridae hosts
Arvicola scherman
Microtus agrestis
Microtus arvalis
Myodes glareolus
Apodemus flavicollis
Apodemus sylvaticus
Rattus norvegicus
Forest
Hedgerow
Num
ber tested for bacteria
Num
ber tested for antibodies
Beyond typical
host habitat
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14
3.2. Extrinsic drivers of pathogen community diversity and composition within rodent community 312
3.2.1. Analyses of pathogen diversity 313
We found evidence for the co-circulation of between 3 (in Mi. agrestis) and 12 (in My. 314
glareolus) pathogen taxa per host species across the rodent community (Figure 1). Using multiple 315
regression analysis on the Shannon diversity index, we found that pathogen diversity differed 316
significantly between habitats (F2,78 = 5.63, p = 0.005) and host species (F5,78 = 7.12, p < 10-4). It 317
was significantly higher in adults than in juveniles (F1,78 = 32.29, p < 10-7). It did not differ between 318
study sites, years, or host sexes (Figure 2, Table S3). Post-hoc Tukey tests showed that after 319
320
-0.55
-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Ar.
sche
rman
Mi.
agre
stis
Mi.
arva
lis
My.
gla
reol
us
Ap.
flav
icol
lis
Ap.
syl
vatic
us
Path
ogen
div
ersi
ty
(resi
dual
Sha
nnon
Inde
x)
bc bc
bc
-0.55
-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Fore
st
Hed
gero
ws
Mea
dow
s
b
a ab
-0.55
-0.5
-0.45
-0.4
-0.35
-0.3
-0.25
-0.2
-0.15
-0.1
-0.05
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Adu
lt
Juve
nile
a
b
A. Host species B. Habitat C. Host age
ab a
c
Figure 2: Extrinsic drivers of pathogen diversity in a rodent species community. Significant differences in Shannon diversity index were tested on residual variance of the multiple regression model controlling for all other extrinsic factors. Different letters signify statistically significant differences at p < 0.05, with post-hoc Tukey adjustments for multi-level factors.
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15
correcting for all other factors in the model, meadow habitats had higher diversity of pathogen 321
exposure than forest habitats, and the diversity of pathogen communities in host species fell along a 322
continuum between My. glareolus (high) and Ar. scherman (low) extremes (Figure 2, Table S3). To 323
understand the relative pathogen diversity of R. norvegicus hosts, excluded from the model because 324
they were entirely confounded with farm habitats, we analyzed two additional modified models : 325
one excluding host habitat and the other excluding host species. Post-hoc Tukey tests from these 326
models respectively showed that R. norvegicus hosts had the second most diverse pathogen 327
community, and that farm habitats were thus in the same high-diversity category as meadow 328
habitats (Table S4). 329
We also found an enormous amount of both bacterial co-infections and concurrent history of 330
viral exposures (Figures 3A, 3B). The percentage of animals co-infected with two or more reliably 331
detectable pathogenic bacterial OTUs among all those infected in each host species ranged between 332
84.4% (in Mi. arvalis) and 10.5% (in Ar. scherman). This co-infection frequency was significantly 333
correlated with the diversity (Shannon Index) of bacteria circulating in each rodent species (Figure 334
3C; Pseudo-R2 = 0.70, p < 0.0001, calculated using logistic regression weighted by the number of 335
infected animals per species). Bacterial co-infections were more frequent than expected in Mi. 336
agrestis, and less frequent than expected in My. glareolus (according to Cook’s Distance, Figure 337
S5A). Results were similar when co-occurrence of anti-viral antibodies were considered along with 338
bacterial OTU exposure (Figure 3D; Pseudo-R2 = 0.55, p < 0.0001). While Mi. arvalis had both 339
more bacterial co-infections and slightly more pathogen co-exposures than expected based on 340
pathogen diversity, other outliers differed between the two measures (Figure S5): My. glareolus co-341
exposure frequencies were not lower than expected, and both Apodemus species had lower than 342
expected co-exposures. 343
3.2.2. Analyses of pathogen community composition 344
Many pathogen taxa were found only in a single host species (Mycoplasma haemomuris OTU 345
Myco8, “Candidatus Mycoplasma ravipulmonis” (Myco10), Brevinema spp., Spiroplasma spp., 346
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16
347
348
349
and LCMV), and each host species had a unique combination of co-circulating pathogens (Figure 350
1). In order to identify extrinsic factors (host species, host sex and age class, habitat, study site, 351
year) potentially driving the composition of pathogen communities within the rodent community, 352
C_Ar_sche C_Mi_arva M_Ap_flav M_Ra_norv
010
2030
4050
60
C_Ar_sche C_Mi_arva M_Ap_flav M_Ra_norv
010
2030
4050
6070
0.5 1.0 1.5 2.0
0.0
0.2
0.4
0.6
0.8
1.0
Relative number of co−circulating pathogen taxa (Shannon Index)
% in
divi
dual
s co−e
xpos
ed (a
mon
g al
l exp
osed
ani
mal
s)
0.5 1.0 1.5 2.0
0.0
0.2
0.4
0.6
0.8
1.0
Relative number of co−circulating bacterial taxa (Shannon Index)
% in
divi
dual
s co
infe
cted
(am
ong
all i
nfec
ted
anim
als)
Num
ber o
f ani
mal
s
0 1 2
3 4
5
Host species Ar. scherman Mi.agrestis Mi.arvalis My.glareolus Ap.sylvaticus Ap.flavicollis R.norvegicus
Num
ber o
f ani
mal
s
0 1 2
3 4
5
Host species Ar. scherman Mi.agrestis Mi.arvalis My.glareolus Ap.sylvaticus Ap.flavicollis R.norvegicus
Prop
ortio
n an
imal
s co
-infe
cted
by ≥
2 ba
cter
ial t
axa
(am
ong
all i
nfec
ted
anim
als)
Relative number of co-circulating
bacterial taxa (Shannon Diversity Index)
A. Number of bacterial infections per animal
B. Number of pathogen co-exposures per animal
C.
D.
Prop
ortio
n an
imal
s ex
pose
d to
≥2
path
ogen
s (a
mon
g al
l exp
osed
ani
mal
s)
Relative number of co-circulating bacterial taxa (Shannon Diversity Index)
Mi. agrestis
Ar. scherman
Mi. arvalis
My. glareolus R. norvegicus
Ap. sylvaticus
Ap. flavicollis
Mi. agrestis
Ar. scherman
Mi. arvalis
My. glareolus
R. norvegicus
Ap. sylvaticus
Ap. flavicollis
Pseudo R2 = 0.55 p < 0.0001
Pseudo R2 = 0.70 p < 0.0001
Figure 3: Bacterial co-infection and co-exposure patterns across host species.
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17
we performed a single MCA on occurrence data for exposure to both bacteria and viruses followed 353
by logistic regression analyses. Due to competing a priori hypotheses that host species and habitat 354
would be important factors, we excluded R. norvegicus individuals (as this host species was the 355
only one found in farm habitats, confounding these two variables; but see MCA results when R. 356
norvegicus was included in Figures S6, S7). Likewise, we excluded pathogens that occurred only in 357
one habitat type of one host species (not including R. norvegicus). Two additional individuals were 358
excluded due to missing sex and age information. Analyses were performed on the remaining 280 359
individuals from six host species and their 14 pathogens (Bartonella, Myco1, Myco2, Myco3, 360
Myco4, Myco6, Myco7, Myco9, Rickettsia, Neoehrlichia, Orientia, Spiroplasma, and antibodies 361
against CPXV and hantaviruses) (Figure S8). 362
Out of 14 orthogonal MCA dimensions returned, the first two captured 23.0% of the variation in 363
pathogen and antibody occurrence, and the first seven explained a cumulative 63.4% of the total 364
variance (Figure S9). Further dimensions captured less variance than would be expected if all 365
dimensions contributed equally to overall inertia in the data. Dimension 1 (MCA Dim1; explaining 366
13.1% of the variation in pathogen community and loading heavily with the presence of Myco1, 367
Myco3 and anti-hantavirus antibodies) differed significantly between host species (F5,268 = 23.83, p 368
< 0.0001) and age classes (F1,268 = 6.27, p = 0.013; Table S5). Dimension 2 (MCA Dim2; 369
explaining 9.9% of the variation in pathogen community and primarily describing the occurrence of 370
Bartonella) was also structured significantly by host species (F5,268 = 3.89, p = 0.002; Table S5). 371
While these first two dimensions varied by host species (Figure 4A) and host age class (Figure 4B), 372
variance in host habitats (Figure 4C) was not significant after accounting for the other factors. Host 373
species was the most consistently important extrinsic driver of pathogen community composition, 374
significantly explaining variation captured in six of the first seven dimensions, MCA Dim1 – MCA 375
Dim7 (except for MCA Dim 5; Table S5). 376
377
378
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18
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
Figure 4. Pathogen comm
unity structure among extrinsic factors (A
) host species (B) host age groups and (C
) habitat types, represented by plotting the m
ean values for the first and second dimensions described by m
ultiple correspondence analysis (MC
A). M
CA
Dim
1 and MC
A D
im 2 collectively
accounted for 23% of total variance in the data. Ellipses include 95%
of individual values for each factor group.
−1.0
−0.5
0.0
0.5
1.0
−1.0−0.5
0.00.5
1.01.5
Dim
1 (14.6%)
Dim2 (11.4%)
GroupsC
_Ar_sche
C_M
i_agre
C_M
i_arva
C_M
y_glar
M_Ap_flav
M_Ap_sylv
MC
A factor map − Biplot
A)
B)
−1.0
−0.5
0.0
0.5
1.0
−1.0−0.5
0.00.5
1.0D
im1 (14.6%
)
Dim2 (11.4%)
GroupsForest
Hedge
Meadow
MC
A factor map − Biplot
C)
−0.5
0.0
0.5
−1.0−0.5
0.00.5
1.0D
im1 (14.6%
)
Dim2 (11.4%)
GroupsAD
ULT
JUVEN
ILE
MC
A factor map − BiplotM
CA
Dim
1 M
CA
Dim
1 M
CA
Dim
1
MCA Dim 2
MCA Dim 2
MCA Dim 2
Host species
Host age group
Habitat type
Mi. arvalis
My. glareolus
Mi. agrestis
Ar. scherm
an A
p. flavicollis A
p. sylvaticus A
dults
Juveniles
Forest
Meadow
Hedgerow
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19
3.3. Associations between pathogens 405
3.3.1. Validation of the associations detected by MCA 406
We applied SCN and GLM analyses to further characterize patterns detected using MCA. Strong 407
and relatively equal loading of MCA Dim1 with Myco1, Myco3, and anti-hantavirus antibody 408
presence indicated that these three pathogens were positively associated with one-another. Indeed, 409
the six animals with anti-hantavirus antibodies were found exclusively in animals infected with 410
Myco3, and Myco1 was found in 2/3 of hantavirus-exposed animals but in just 1/3 of those without 411
hantavirus exposure. The MCA also revealed significant differences among host species and host 412
age classes for Dim 1; hantavirus and Myco 3 only circulated in two host species (Mi. arvalis and 413
My. glareolus) and 34 of those 35 occurrences were in adults. To exclude positive associations 414
arising from mutual host specificity and age-related accumulation of exposure probability, we 415
focused our analyses on the dataset restricted to adults of the two host species in which all three 416
pathogens co-circulated (Mi. arvalis and My. glareolus). Individual SCN analyses performed on 417
adults of each host species revealed no associations (Table S6A). However, since values of MCA 418
Dim 1 did not differ between the two host species (according to post-hoc tests given in Table S5), 419
we also ran a single SCN analysis on the pooled data from adults of both species to improve 420
statistical power (Table S6A). According to this pooled SCN analysis, the three-way co-occurrence 421
of Myco1, Myco 3 and anti-hantavirus antibodies was significantly more frequent than would be 422
expected by random chance (p = 0.008), with a trend for anti-hantavirus antibodies occurring by 423
themselves more rarely than expected (p = 0.13; Table 6A, Figure S10). We also investigated this 424
association using GLM. However given the small number of hantavirus exposures and perfect 425
association with Myco3 infection, there was insufficient statistical power to explicitly test for an 426
association between all three pathogens and extrinsic factors. We therefore ran three reciprocal 427
GLM models on the restricted dataset, one for each pathogen as a function of extrinsic factors 428
controlling for heterogeneous host groups (host species, host sex, study site, habitat, and year 429
sampled) and exposure to the two other pathogens (Table S6B). These models showed that there 430
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20
remained significant unexplained positive associations between hantavirus exposure and Myco1 431
infection (Hantavirus antibodies ~ Myco1: χ2 = 5.67, p = 0.017; Myco1 ~ Hantavirus antibodies: χ2 432
= 2.89, p = 0.09) and between hantavirus exposure and Myco3 infection (Myco3 ~ Hantavirus 433
antibodies: χ2 = 12.11, p < 0.001), but that there was no evidence of direct association between 434
Myco1 and Myco3 infections (Myco1 ~ Myco3: χ2 = 0.07, p = 0.8; Myco3 ~ Myco1: χ2 < 0.01, p = 435
0.96; Figure 5A). 436
437
438
439
The third MCA dimension also presented a clear hypothesis with sufficient statistical power to 440
be tested. MCA Dim3 was characterized by co-variation in Myco2 (M. haemomuris) and Myco4 441
(M. coccoides) infections suggesting a positive association between members of these two 442
Mycoplasma species. Myco2 and Myco4 OTUs co-circulated only in Ap. sylvaticus hosts, thus we 443
limited our analysis to this host species. There was no significant association between the two 444
OTUs detected by SCN analysis (Table 7A), and after correcting for all extrinsic factors, there 445
R. norvegicus
A.
Myco1
Hantavirus antibodies
Myco3
My. glareolus
Mi. arvalis
B.
CPXV antibodies
Bartonella spp.
Ar. scherman
C.
Mycoplasma coccoides (Myco4,6)
Mycoplasma haemomuris (Myco1,2,3,5,7,8,9)
D.
Mycoplasma coccoides Mycoplasma haemomuris
(Myco1 – Myco9)
Bartonella spp.
My. glareolus
My. glareolus
Ap. flavicollis
Ap. sylvaticus
+ +
+
+
-
+ - Positive association
Negative association
Figure 5: Associations between pathogens in a community of rodents. Association hypotheses were generated by multiple correspondence analysis (A) or previously noted in the literature (B, C, D). Only associations supported by significant statistical tests (p < 0.05) are illustrated. Red arrows represent positive associations, blue arrows represent negative associations.
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21
remained only a non-significant trend (Myco2 ~ Myco4: χ2 = 2.47, p = 0.12; Myco4 ~ Myco2: χ2 = 446
2.47, p = 0.12; Table S7B) for a positive association between the two OTUs. No additional 447
associations with sufficient variance for statistical tests were clearly suggested by the MCA 448
analysis. 449
450
3.3.2. Validation of associations described in the literature 451
We tested the a priori hypothesis that seropositivity to CPXV would be positively associated 452
with Bartonella infection, previously detected in Mi. agrestis (Telfer et al. 2010). The whole dataset 453
was considered as these two pathogens co-circulated in all host species (Figure 1). SCN analyses 454
performed independently for each host species revealed no associations (Table S8A), and the MCA 455
results suggested that pooling data across host species would be inappropriate. After correcting for 456
extrinsic factors using GLM, we found reciprocal evidence for a positive association in Ar. 457
scherman hosts (Bartonella ~ CPXV antibodies: χ2 = 5.37, p = 0.020; CPXV antibodies: χ2 = 5.21, 458
p = 0.022; Figure 5B), but not in any other host species (Table S8B). It is of note that there were 459
only eight Mi. agrestis individuals, rendering statistical power to test for the association while 460
controlling for extrinsic factors insufficient in this host species where the association was 461
previously described. While prevalence of both pathogens in Mi. agrestis was relatively high 462
compared to other host species, one of the two animals without Bartonella infection was positive 463
for CPXV antibodies, also precluding evidence for a within-species trend. 464
We next focused on the potential associations between OTUs identified as belonging to two 465
different species of hemotropic Mycoplasma, M. haemomuris (HM) and M. coccoides (HC), within 466
the four host species in which they both circulated (My. glareolus, Ap. flavicollis, Ap. sylvaticus, R. 467
norvegicus; Figure 1; Figure S4). We found no significant associations using independent SCN 468
analyses for each host species (Table S9A). However, after controlling for extrinsic factors using 469
GLM, a significant positive association was detected (HM ~ HC: χ2 = 10.27, p = 0.0013; HC ~ HM: 470
χ2 = 9.92, p = 0.0016), and did not differ between host species (non-significant interaction term 471
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22
between host species by explanatory pathogen occurrence in each reciprocal model, Table S9B; 472
Figure 5C). We note that only one R. norvegicus animal was uninfected with M. haemomuris, and 473
that animal also had no M. coccoides infection; thus the trend for the association in this host species 474
was also positive but lacked sufficient variance for independent statistical analysis. 475
Finally, we tested for associations between Bartonella spp. and hemotropic Mycoplasma species, 476
grouping the occurrence of different OTUs of the latter (Myco1 – Myco9) into a single presence-477
absence variable. There was no association detected by SCN analyses (Table S10A), and marginal 478
evidence that any association may differ by host species after correcting for extrinsic factors using 479
GLM (Table S10B). After controlling for extrinsic factors using independent GLMs for each host 480
(where possible), we found a negative association between the two pathogen groups only in My. 481
glareolus hosts (Bartonella ~ Mycoplasma: χ2 = 5.73, p = 0.017; Mycoplasma ~ Bartonella: χ2 = 482
5.65, p = 0.017, Table S10B, Figure 5D). 483
484
4. Discussion 485
Rodents have long been recognized as important reservoirs of infectious agents, with a high 486
transmission potential to humans and domestic animals. Europe is identified as a hotspot of rodent 487
reservoir diversity and one third of rodent species are considered hyper-reservoirs, carrying up to 11 488
zoonotic agents (Han et al. 2015). Nevertheless, associations between these pathogens have still 489
only rarely been investigated (but see, for example, studies from field voles in the UK (Telfer et al. 490
2010) and in Poland (Pawelczyk et al. 2004), gerbils in Israel (Cohen et al. 2015), across a rodent 491
community in North America (Dallas et al. 2019), and co-infection frequencies of zoonotic 492
pathogens from rodents in Croatia (Tadin et al. 2012)). 493
In this study, we confirmed that rodent communities in northern France may harbor a large 494
diversity of potential zoonotic pathogens, with at least 10 bacterial genera (23 OTUs) and 495
antibodies against four genera of viruses circulating in the areas examined. Some of these pathogens 496
have already been reported in this region or in geographic proximity, including viruses 497
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23
(Orthohantavirus, Orthopoxvirus, Mammarenavirus (Charbonnel et al. 2008; Salvador et al. 498
2011)); and bacteria (e.g. Bartonella, Mycoplasma, Rickettsia, “Candidatus Neoehrlichia”, 499
Orientia, Spiroplasma, Treponema, Leptospira, Borrelia, Neisseria, Pasteurella; see (Vayssier-500
Taussat et al. 2012; Razzauti et al. 2015)). Several previously unseen agents were also detected, 501
such as Ljungan virus, a picornavirus found in other European rodent populations (e.g., Salisbury et 502
al. 2014), and a relative of the putatively pathogenic spirochaete Brevinema andersonii that infects 503
short-tailed shrews and white-footed mice in North America (Defosse et al. 1995). In addition, we 504
detected a relatively high prevalence of anti-hantavirus antibodies in Mi. arvalis, explained by 505
cross-reactivity between the anti-PUUV antibodies used in our assay and those elicited against the 506
related Tula orthohantavirus (TULA) virus common to European voles (Deter et al. 2007; 507
Tegshduuren et al. 2010). 508
Three pathogens were highly prevalent. Anti-Orthopoxvirus antibodies were detected in all 509
seven focal rodent species, with prevalence levels reaching up to 70%. This corroborates prior 510
evidence that cowpox virus could be widespread in European rodents, particularly voles (Bennett et 511
al. 1997; Essbauer, Pfeffer & Meyer 2010; Forbes et al. 2014). An astounding 77% of all 512
individuals in the study were infected by Bartonella sp., with R. norvegicus being the notable 513
exception (with only 10% infected, leaving an average of 84% prevalence in all other species). The 514
bacterial genus Bartonella is a diverse group of hemotrophs known to commonly infect rodents and 515
other mammals (Breitschwerdt & Kordick 2000; Bai et al. 2009) and which have also been 516
implicated in both zoonotic and human-specific disease (Iralu et al. 2006; Breitschwerdt 2014; 517
Vayssier-Taussat et al. 2016). We could not assess the specific diversity of Bartonella sp. 518
circulating in these rodent communities because accurate resolution in this genus requires additional 519
genetic markers (Matar et al. 1999; Guy et al. 2013). Hemotropic and pneumotropic Mycoplasma 520
spp. were also highly prevalent, collectively infecting 43% of all hosts, with nearly all R. 521
norvegicus and Mi. arvalis samples infected. On the contrary, we found just one Mycoplasma 522
infection in Ar. scherman, despite ample sampling (70 animals tested) and its previous detection in 523
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24
A. scherman from Eastern France (Villette et al. 2017). These Mycoplasma species are also known 524
pathogens of humans and rodents (Harwick, Kalmanson & Guze 1972; Baker 1998). Here, we 525
found 15 different OTUs corresponding to two distinct hemotropic Mycoplasma species (M. 526
haemomuris and M. coccoides) and the pneumotropic Mycoplasma species M. pulmonis and 527
“Candidatus M. ravipulmonis”. The former two are both hemotropic mycoplasmas responsible for 528
vector-transmitted infectious anaemia of wild mice, rats, and other rodent species (Neimark et al. 529
2001, 2005; Messick 2004). However, M. pulmonis and “Candidatus M. ravipulmonis” cause 530
respiratory infections, are more closely related to other pneumotropic mycoplasmas, and 531
“Candidatus M. ravipulmonis” has only ever before been described as spontaneously occurring in 532
laboratory mice and rats exposed to unknown microbes of other animals and humans via passaging 533
experiments (formerly termed Grey Lung virus; (Andrews & Glover 1945; Neimark, Mitchelmore 534
& Leach 1998; Graham & Schoeb 2011; Piasecki, Chrzastek & Kasprzykowska 2017)). 535
Lastly, our data corroborated the status of hyper-reservoir (more than two zoonotic pathogens 536
carried by a reservoir species) for all seven of the focal rodent species studied here (Han et al. 537
2015), with the addition of Mi. subterraneous that we also found to carry two potentially zoonotic 538
pathogens (Bartonella spp. and Brevinema spp.; Appendix 1). Overall, we found a high variability 539
in the number of pathogens circulating in each species despite correction for sampling effort, with 540
low levels observed in Apodemus species and Arvicola scherman, and high levels detected in Mi. 541
arvalis, My. glareolus, and R. norvegicus. 542
The search for factors that drive parasite species richness, diversity and community composition 543
has been at the core of numerous studies (Poulin 1995; Poulin & Morand 2000; Nunn et al. 2003; 544
Mouillot et al. 2005; Krasnov et al. 2010). Here, we emphasized that both pathogen diversity and 545
community composition was mainly structured by host species identity, despite both shared habitats 546
and shared pathogen taxa. Using multiple correspondence analysis (MCA) together with logistic 547
regression and the novel computational method of association screening (SCN) analysis, we found 548
no evidence that any specific pathogen-pathogen associations were likely to be as important as host 549
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25
species identity in determining pathogen distributions across the community of rodents. The strong 550
influence of host characteristics (Cohen et al. 2015) and host species identity (Dallas et al. 2019) on 551
pathogen community composition has recently been described in comparison to intrinsic pathogen-552
pathogen associations in other rodents. Moreover, the pathogen community composition provided a 553
unique signature of each rodent species, even among those most closely related (e.g. Ap. flavicolis 554
and Ap. sylvaticus). This result is in line with the conclusions of recent meta-analyses showing that 555
phylogeny, over other host traits, had a minimal impact on pathogen diversity in rodent species 556
(Luis et al. 2013; Guy et al. 2019). 557
The importance of host species identity in shaping pathogen community composition may not 558
stem from strict host-pathogen specificity, as most pathogens were found to infect multiple host 559
species – a broad result echoed across animal communities (Taylor et al. 2001; Woolhouse, Taylor 560
& Haydon 2001; Cleaveland, Laurenson & Taylor 2001; Pedersen et al. 2005; Streicker et al. 561
2013). However, we might be cautious as more precise molecular analyses are necessary to test 562
whether different species of a bacteria genus or divergent populations of the same bacteria species 563
may circulate independently in different rodent host species, with little or no transmission. For 564
example, two genera seemed to be largely shared among the rodent species studied here, Bartonella 565
and Mycoplasma. But previous studies have shown strong host-specificity when considering the 566
genetic variants of Bartonella (Buffet et al. 2013; Withenshaw et al. 2016; Brook et al. 2017) 567
Evidence in the literature for host specificity of Mycoplasma species has led to a mix of conclusions 568
(Pitcher & Nicholas 2005), as cases of cross-species transmission are commonly reported – 569
particularly in humans – despite a general consensus that most species are highly host-specific. We 570
found that some Mycoplasma taxa were dominant contributors to prevalence in a single host 571
species, and that when shared, they were shared with just a few other specific host species. Rare 572
infections in unexpected host species (e.g., Myco6 in Ar. scherman and Myco1 in Ap. flavicollis) 573
were represented by fewer sequence reads compared to positive samples in host species where they 574
were more prevalent, suggesting a low potential for amplification and sustained transmission from 575
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26
these occasional hosts (Figure S4). On the other hand, while the Cricetidae appeared to be 576
susceptible only – with rare exception – to taxa within the M. haemomuris group, host species in the 577
Muridae family were susceptible to all three distinct Mycoplasma species detected, The biggest 578
exception to this pattern was that three of 62 Myodes glareolus (the most basal member of the 579
Cricetidae included in the study) animals were found to be infected by both hemotropic 580
Mycoplasma species. These results both support the observation that cross-species transmission 581
naturally occurs among wild rodents and suggest that the degree of host specificity may be driven 582
by both host and pathogen factors. 583
Several studies have emphasized the influence of host habitat specialization on parasite species 584
richness, low habitat specialization being associated with both high species richness of macro- and 585
micro-parasites (e.g. (Morand & Bordes 2015)). Our results did not fully corroborate this 586
association; while the grassland-specific Ar. scherman had the lowest pathogen diversity and the 587
multi-habitat spanning My. glareolus had the highest pathogen diversity, entirely farm-dwelling R. 588
norvegicus had high pathogen diversity nearly equal to that of My. glareolus, and the two 589
Apodemus hosts (neither with significantly higher pathogen diversity than Ar. scherman) were 590
found across both meadows and hedgerows. We also found no influence of sampling sites or years 591
on pathogen diversity at the small spatio-temporal scale considered here. Hence, further research is 592
required to decipher the eco-epidemiological features that explain this strong influence of rodent 593
species on pathogen community composition, such as host densities, home ranges, behaviours, or 594
genetics (e.g. (Morand 2015). 595
We found that a large proportion of rodents from Northern France were co-infected with two or 596
more bacterial pathogens (max = 5 bacterial pathogens in Mi. arvalis and R. norvegicus hosts), and 597
also had concurrent histories of exposure to multiple viruses (max = 5 bacterial and/or viral co-598
exposures in Mi. arvalis, My. glareolus, Ap. sylvaticus, and R. norvegicus hosts). The percentage of 599
pathogen co-exposed hosts was as high as 89 % (in Mi. arvalis hosts), dropping only to 83% when 600
considering only presently co-infecting bacterial taxa. These results are in line with recent studies 601
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27
that have shown that co-infections by multiple pathogens are common in natural populations (e.g. in 602
mammals, birds, amphibians, ticks, humans (Telfer et al. 2010; Griffiths et al. 2011; Moutailler et 603
al. 2016; Clark et al. 2016; Stutz et al. 2018)). Here, we also observed high variability in the 604
percentage of individuals with evidence of multiple current or prior infections between the rodent 605
species investigated. This variation in pathogen co-exposure was highly correlated to the diversity 606
of pathogens circulating in each host species, suggesting the dominance of a random process of 607
pathogen exposure for each individual. However, there were a few intriguing outliers: My. glareolus 608
hosts were less co-infected than expected based on diversity of bacterial taxa, but not when viral 609
antibodies were included; conversely, Ar. scherman hosts were more co-exposed when viruses were 610
considered, but not when only bacteria were considered; and M. arvalis hosts had consistently 611
higher proportions of co-exposures whether viruses were or were not considered along with 612
bacteria. The non-random grouping of pathogen exposures within individuals (as in M. arvalis) may 613
result from heterogeneity in extrinsic transmission, environmental, or susceptibility factors 614
(Cattadori et al. 2006; Swanson et al. 2006; Beldomenico et al. 2008; Fenton, Viney & Lello 2010; 615
Beldomenico & Begon 2010) or from intrinsic interactions between pathogens (e.g. facilitation 616
mediated by hosts immune response). Differences in the pattern of co-exposure frequencies when 617
including or excluding antiviral antibodies (as with M. glareolus and Ar. scherman) could result 618
from different mechanisms (e.g., bacterial manipulation of innate immunity (Diacovich & Gorvel 619
2010)) affecting pathogen community assemblage. However, a lack of deviance from the expected 620
co-exposure frequency does not exclude the possibility that both extrinsic and intrinsic processes 621
may be occurring. 622
Here, we tested for pathogen-pathogen associations suggested by multiple correspondence 623
analysis (MCA) of the structure of pathogen exposures in this specific rodent community, as well as 624
those previously described in the literature. We used multiple logistic regression analyses (via 625
analysis of deviance on generalized linear models (GLMs)) to determine whether pathogen-626
pathogen associations were present after extrinsic factors creating heterogeneity among hosts were 627
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28
taken into account. We found evidence in support of three previously identified associations 628
(positive association between M. haemomuris and M. coccoides infections; positive association 629
between Bartonella infection and the presence of CPXV antibodies; negative association between 630
Bartonella and hemotropic Mycoplasma infections), and characterized one set of associations not 631
previously described (positive associations between the presence of hantavirus antibodies and 632
infections by two specific M. haemomuris OTUs) – each in a unique subset of host species. 633
The positive association between Mycoplasma haemomuris and M. coccoides was found in the 634
four rodent species where these bacteria co-circulate (i.e., My. glareolus, Ap. flavicollis, Ap. 635
sylvaticus and R. norvegicus). Transmission of Mycoplasma blood infections is not yet well 636
understood, but given a lack of support for density-dependent transmission and high incidence of 637
spill-over events, it is likely that these bacteria are transmitted through bites of blood-sucking 638
arthropod vectors (Volokhov et al. 2017). The positive associations detected between M. 639
haemomuris and M. coccoides could also result from similarities in rodent susceptibility. Indeed 640
Mycoplasma infection can lead to acute or chronic infection, and the establishment of chronic 641
bacteremia seems to occur in immunosuppressed or immunocompromised individuals (Cohen et al. 642
2018). Co-infections with multiple Mycoplasma spp. might therefore be more likely to be detected 643
in these immunocompromised rodents with chronic infections. The existence of chronic infections 644
might also lead to additional co-infections and positive associations as a result of disease-induced 645
changes in population dynamics, immune system function, or through direct pathogen-pathogen 646
interactions (Fenton 2008; Aivelo & Norberg 2018; Fountain-Jones et al. 2019). 647
Whether through the accumulation of exposure probabilities or increased susceptibility, the 648
previously-undocumented positive association we found here between M. haemomuris OTUs 649
(Myco1 and Myco3) and hantavirus antibodies may similarly be explained by the chronic nature of 650
both Mycoplasma and hantavirus infections in rodents (e.g. for Puumala hantavirus in bank voles 651
(Yanagihara, Amyx & Gajdusek 1985; Meyer & Schmaljohn 2000; Vaheri et al. 2013)). This 652
positive association was found in both host species where the majority of hantavirus exposures 653
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29
occurred (Microtus arvalis and Myodes glareolus), consistent with the generality of association 654
between Mycoplasma species across host taxa detailed above, suggesting the intrinsic ecology of 655
these pathogens contributes to shaping variation in the pathogen community independent of host 656
species identity. Curiously, we found no evidence for direct associations between OTUs of the same 657
Mycoplasma species, thus facilitation interactions are unlikely to explain the high diversity of 658
Mycoplasma taxa both within and between host species. 659
Infections by Bartonella species are also known to often result in subclinical and persistent 660
bacteremia in mammals, including rodents (Birtles et al. 2001; Kosoy et al. 2004). The positive 661
association detected in Ar. scherman between Bartonella spp. and cowpox virus antibodies might 662
therefore be explained by, for example, joint accumulation of both chronic bacterial infections and 663
long-lived antibodies used to test for prior exposure to relatively short-lived CPXV virus infections. 664
However, if the same processes governing association of the chronic infections described above 665
were at play here, we would have expected to find both pathogens implicated in positive 666
associations (i) with other chronic infections, and (ii) across host species given their ubiquitous 667
prevalence. While the failure to recover the association in Mi. agrestis (where it was previously 668
described (Telfer et al. 2010) was likely due to low statistical power, the lack of a general pattern 669
across other host species despite adequate sampling suggests a more specific, and potentially 670
immune-mediated, ecological process between these two pathogens. Indeed, pox virus infections, 671
including CPXV, have been shown to induce immunomodulation that increases host susceptibility 672
to other parasites (Johnston & McFadden 2003). These interactions could be of variable intensities 673
according to the rodent species considered, due to potential differences in impacts of CPXV 674
infection on immunity across host species, or to the influence of other infections not examined here 675
on host immune responses during pox infections (e.g. helminths (Cattadori, Albert & Boag 2007), 676
protozoa (Telfer et al. 2010)). Furthermore, Bartonella infection was negatively associated with 677
Mycoplasma infections in My. glareolus, corroborating negative interactions revealed in co-678
infection experiments in gerbils (Eidelman et al. 2019). This association may therefore originate 679
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30
from an interaction mediated by specific (immune-) genetic features of My. glareolus, and not 680
ecological conditions as proposed by Eidelman et al. (2019). The antagonistic and host-specific 681
nature of this association lends further support to the interpretation that Bartonella infections do not 682
behave in similar ways to other chronic infections in the community. However, few studies have 683
investigated the robustness of within-host interactions across different host species (e.g., (Lello et 684
al. 2018)), and this question deserves further investigation. 685
Our results suggest that intrinsic ecological interactions could help shape the composition of the 686
pathogen community within hosts. However, this suggestion provides only a hypothesis that 687
requires further investigation to establish its provenance. Interpretation of associations can be 688
misleading, as they may arise from unmeasured co-factors such as exposure to shared transmission 689
routes, and may even run counter to the underlying ecological process (Fenton et al. 2014). We 690
noted that the associations we found here were considered properly characterized only once 691
extrinsic factors had been taken into account using multiple logistic regression analyses, and were 692
not visible (or even misleading, in the case of a 3-way interaction between hantavirus, Myco1 and 693
Myco3) when ignoring these factors using the SCN analysis, despite the increased statistical power 694
it offered. Evidence for interactions between pathogens within hosts initially came from laboratory 695
studies (e.g. in the development of vaccines, reviewed in (Casadevall & Pirofski 2000)), and until 696
recently, many studies conducted in the wild could not detect such interactions (e.g. (Behnke 697
2008)). Developments of statistical approaches have contributed to improve sampling designs and 698
analyses, in particular by better controlling for confounding factors, enabling the detection of 699
associations resulting from these within-host interactions (e.g., (Lello et al. 2004; Telfer et al. 700
2010)). Experiments conducted in semi-controlled environments have been used to confirm the 701
importance of interactions suggested by the associations (e.g. (Knowles et al. 2013)). Both 702
facilitation mediated by immune responses (e.g., (Ezenwa et al. 2010)) and competition mediated 703
by shared resources (e.g., (Brown 1986; Budischak et al. 2018)) have been emphasized. 704
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31
There remain additional important limits to the interpretation of snapshot observational studies 705
from wild populations such as we have presented here. For instance, they can not provide 706
information about the sequence and timing of infection, although these features strongly affect the 707
outcome of within-host interactions (Eidelman et al. 2019). Moreover, both the 16S metabarcoding 708
approach and serological antibody tests can only be interpreted in terms of presence/absence of 709
exposure to pathogens, although co-infection may rather impact parasite abundance (e.g., (Thumbi 710
et al. 2013; Gorsich, Ezenwa & Jolles 2014)). Lastly, we also acknowledge several caveats to 711
consider with our methods. We removed animals from which fewer than 500 reads were amplified 712
in one or both bacterial metabarcoding PCR replicates. While 16 of these samples removed were 713
due to random failure of PCR amplification from just one of the two replicates, 12 of the animals 714
had poor amplification in both PCR replicates. In the absence of an internal positive control, e.g. a 715
spike-in standard (Zemb et al. 2020), we were unable to verify whether a lack of reads was due to 716
poor DNA extraction or a true lack of infections. Although this has a risk of artificially inflating 717
prevalence rates by selectively removing uninfected individuals, it is unlikely to have had a 718
qualitative effect on our results. Similarly, limiting our analyses to OTUs with 500 reads or more in 719
the entire dataset may select against detection of very rare or low-burden infections. We also 720
removed many OTUs corresponding to bacteria normally occurring in external or internal 721
microbiomes of healthy animals, some of which were represented by a high abundance of reads in 722
positive animals. We know that, for instance, Helicobacter species are naturally found in the 723
digestive tract, but can also cause pathogenic infections. We also know that parasitism can affect 724
host microbiome composition (Gaulke et al. 2019), and this in turn can have impacts on host health 725
and disease susceptibility (reviewed in (Murall et al. 2017)). Thus, our choice to ignore OTUs 726
corresponding to microbes typical of healthy flora contributes to the problem of missing data, such 727
as information on intestinal helminth infections, which may explain or alter the associations we 728
were able to detect. These caveats are common problems for disease surveillance and community 729
ecology studies, irrespective of the diagnostic methods, and it is difficult to speculate about their 730
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32
overall impacts on the present study. Finally, it is well-understood that this bias towards detection 731
of common pathogens and difficulty in interpreting evidence for the absence of a pathogen in a 732
given individual or population can make testing for negative associations driven by antagonistic 733
ecological interactions incredibly difficult, if not impossible (Weiss et al. 2016; Cougoul et al. 734
2019). 735
736 Conclusions 737
Our results add to a growing number of studies finding that (i) rodents host many important 738
zoonotic human pathogens and (ii) pathogen communities are shaped primarily by host species 739
identity. We also detected a number of previously un-described associations among pathogens 740
within these rodent communities, and we also confirmed previously identified associations, 741
sometimes in other rodent species than those in which they were previously described. These 742
associations can be considered in the future as hypotheses for pathogen-pathogen interactions 743
within rodents hosts, and that participate in shaping the community of pathogens in rodent 744
communities. Long-term survey and experimental studies are now required to confirm these 745
interactions and understand the mechanisms underlying the patterns of co-infection detected. In 746
addition to these biological results, we have identified several methodological caveats, with regard 747
to both pathogen and association detection, that deserves further investigation to improve our ability 748
to make robust inference of pathogen interactions. 749
750
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33
Acknowledgements 751
We would like to thank all collaborators and research assistants that previously helped with the field 752
work (Yannick Chaval, Cécile Gotteland, Marie-Lazarine Poulle). This study was partly funded by 753
the INRA Metaprogram MEM Hantagulumic, and by the European programme FP7-261504 754
EDENext. The manuscript is registered with the EDENext Steering Committee as EDENext409. 755
None of the rodent species investigated here has protected status (see list of the International Union 756
for Conservation of Nature). All procedures and methods were carried out in accordance with 757
relevant regulations and official guidelines from the American Society of Mammalogists. All 758
protocols presented here were realized with prior explicit agreement from relevant institutional 759
committee (Centre de Biologie pour la Gestion des Populations (CBGP): 34 169 003). 760
We thank Hélène Vignes of the Cirad Genotyping platform for her assistance with the MiSeq 761
sequencing, and Sylvain Piry, Alexandre Dehne-Garcia and Marie Pagès for their help with the 762
bioinformatic analysis. 763
764
Data Accessibility 765
Supplementary data deposited in Dryad (https://doi.org/XXXXXXXXXXX) include the following 766
16S metabarcoding data: (i) raw sequence reads (fastq format), (ii) raw output files generated by the 767
mothur program (iii) raw abundance table and (iv) filtered occurrence table. 768
769
770
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34
Table 1. BLAST search results for OTUs suspected of belonging to pathogenic genera. 771 772
773
Infecting)species)identity
OTU)Number
Nuimber)of)Reads
Genbank)Accession)Number BLAST)results)(%)identity) Pathogen)Code
Bartonella)spp. Otu00001 6353372 MT027154 100%2Bartonella)grahamii2(AB426637)2from2wild2North2America2rodents;299%G100%2identity2to2many2other2pathogenic2Bartonella)species.
Bartonella
Brevinema)spp. Otu00123 5603 MT027155 97%2Brevinema)andersonii)(NR_104855)2type2sequence,2infectious2spirochaete2of2shortGtailed2shrew2and2whiteGfooted2mouse2in2North2America
Brevinema
Candidatus)Neoehrlichia2mikurensis
Otu00039 18358 MT027156 100%2Candidatus2Neoehrlichia2mikurensis2(KF155504)2tickGborne2rodent2disease,2opportunitstic2in2humans
Neoehrlichia
Mycoplasma)ravipulmonis
Otu00054 6086 MT027164 100%2Mycoplasma2ravipulmonis2(AF001173),2grey2lung2disease2agent2in2Mus2musculus;288%2M.)orale2from2humans2(LR214940)
Myco10
Mycoplasma)spp. Otu00004 845971 MT027157 99%2identity2to2uncultured2Mycoplasma2species2(KU697344)2from2small2rodents2in2Senegal2and2uncultured2eubacterium22(AJ292461)2from2Ixodes2ticks;295%2(KM538694)2and294%2(MK353834)2identity2to2uncultured2hemotropic2Mycoplasma2species2in2European2and2South2American2bats
Myco1
Mycoplasma)spp. Otu00003 426034 MT027158 99%)Mycoplasma)haemomuris2(AB758439)2from2Rattus)rattus Myco2Mycoplasma)spp. Otu00006 106443 MT027159 99%2uncultured2Mycoplasma2spp.2(KP843892)2from2leopards2in2
South2Korea;299%2identity2to2uncultured2Mycoplasma2(KT215637)2from2rodents2in2Brazil
Myco3
Mycoplasma)spp. Otu00010 72724 MT027160 99%2Mycoplasma)coccoides2comb.2nov.2(AY171918);297%)Candidatus2Mycoplasma2turicensis2(KJ530704)2from2Indian2mongoose
Myco4
Mycoplasma)spp. Otu00005 165095 MT027161 100%2Mycoplasma)haemomurisGlike2undescribed2species2(KJ739312)2from2Rattus)norvegicus
Myco5
Mycoplasma)spp. Otu00007 92237 MT027162 99%2uncultured2Mycoplasma2spp.2(KC863983)2from2Micromys)minutus2(eurasian2harvest2mouse)2in2Hungary;298%2M.)coccoides)comb.2nov.(AY171918)
Myco6
Mycoplasma)spp. Otu00012 39767 MT027163 93%2uncultured2Mycoplasma)species2(KU697341)2of2mice2in2Senegal;292%2Mycoplasma2spp.2(KP843892)2from2leopards2in2South2Korea,291%2Mycoplasma)haemomuris)(AB820289)2in2rats
Myco7
Mycoplasma)spp. Otu00015 31528 MT027165 98%2uncultured2Mycoplasma)spp.2(KT215632)2from2wild2rodent2spleen2in2Brazil;295%2uncultured2Mycoplasma2spp.2(KF713538)2in2little2brown2bats
Myco8
Mycoplasma)spp. Otu00049 40125 MT027166 96%2uncultured2Mycoplasma2spp.2from2Brazilian2rodents2(KT215638)2and2S.2Korean2leopard2(KP843892)
Myco9
Orientia)spp. Otu00111 876 MT027167 97%2Orientia)tsutsugamushi)(KY583502)2from2humans2in2India,2zoonotic2Rickettsial2pathogen2(causes2scrub2typhus)
Orientia
Rickettsia)spp. Otu00008 72098 MT027168 98%2Rickettsia)japonica)(MF496166)2which2causes2Japanese2spotted2fever,)R.)canadensis2(NR_029155)2&2R.)rhipicephali2(NR_074473)2type2strains
Rickettsia
Spiroplasma)spp. Otu00093 4738 MT027169 95%2uncultured2Spirolasma)spp.2(KT983901)2from2Ixodes2tick2on2a2dog;294%2identity2to2type2strain2of2Spiroplasma)mirum2(NR_121794),2the2agent2of2suckling2mouse2cataract2disease;2Spiroplasma)ixodetis)causes2similar2disease2in2humans.
Spiroplasma
Arcobacter)cryaerophilus
Otu00296 403 MT027170 100%2Arcobacter)cryaerophilus)(CP032825)2emerging2enteropathogen2in2humans,2zoonotic,2pathogenic2in2rats
Arcobacter
Borrelia)miyamotoi Otu00318 419 MT027171 100%2Borrelia)miyamotoi2(CP010308)in2humans2and2Ixodes,2zoonotic2pathogen
Borrelia1
Borrelia)spp. Otu00514 206 MT027172 96%2Borrelia2sp.2nov2"Lake2Gaillard"2in2Peromyscus)leucopus2(AY536513),295%2B.)hermsii2(MF066892)2from2tick2(Ornithodoros)hermsi)2bites2in2humans
Borrelia2
Borrelia)afzelii Otu00071 78 MT027173 100%)Borrelia)afzelii2(CP009058)2human2pathogen2closely2related2(98%)2to2B)burgdorferii2(positive2control2sequence)
Borrelia3
Leptospira)spp. Otu01015 257 MT027174 100%2several2pathogenic2Leptospira2species2from2mammals,2e.g.,2L.)interrogans)(LC474514)2in2humans
Leptospira
Mycoplasma)pulmonis
Otu00771 255 MT027175 100%2Mycoplasma)pulmonis2(NR_041744)2chronic2respiratory2pathogen2of2mice2and2rats
Myco0771
Mycoplasma2spp. Otu04125 164 MT027176 90%2Mycoplasma)ravipulmonis2(AF001173),2grey2lung2disease2agent2in2Mus)musculus;280%2M.)phocidae2from2California2sea2lions2(DQ521594)
Myco4125
Eukaryotic2family2Sarcocystidae
Otu00056 8684 XXXXXX 97%2similar2to2plastid2small2ribosomal2unit2of2Hyaloklossia)lieberkuehni2(AF297120),2a2parasitic2protazoa2of2European2green2frog;2296%2Noespora)canim2(MK770339)2&2Sarcocystis)muris2(AF255924);295%2Toxoplasma)gondii)(TGU28056)
Sarcocystidae1
Eukaryotic2family2Sarcocystidae
Otu00191 3678 XXXXXX 92%2Neospora)caninum2(MK770339)2parasite;290%2Toxoplasma)gondii))(U87145)2zoonotic2pathogen
Sarcocystidae2
Eukaryotic2family2Sarcocystidae
Otu00254 1219 XXXXXX 98%2Sarcocystis muris (AF255924)2coccidian2parasite2first2found2in2mice
Sarcocystidae3
Pathogenic+taxa,+reliably+detectable
Pathogenic+taxa,+not+reliably+detectable
preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for thisthis version posted February 10, 2020. . https://doi.org/10.1101/2020.02.09.940494doi: bioRxiv preprint
35
Table 1 (continued). BLAST search results for OTUs suspected of belonging to pathogenic genera. 774 775
776
Infecting)species)identity
OTU)Number
Nuimber)of)Reads
Genbank)Accession)Number BLAST)results)(%)identity) Pathogen)Code
Corynebacterium.xerosis
Otu00050 1853 MT027177 100%.Corynebacterium.xerosis.(MH141477),.only.opportunistic.infections.identified
Corynebacterium
Dietzia.spp. Otu00102 2626 MT027178 100%.Dietzia.spp..e.g.,.D..aurantiaca.(MK25331);.common.contaminant;.opportunistic.in.humans;.thoguht.to.outJcompete.Trypanosomes
Dietzia
Helicobacter.spp. Otu00013 34894 MT027179 96%.homology.to.Helicobacter.suncus..(AB006147).isolated.from.shrews.with.chronic.gastritis;.95%.identity.to.type.speciment.for.H..mustelae.(NR_029169).which.causes.gastritis.in.ferrets;.but.could.be.normal.gut.flora
Helico1
Helicobacter.spp. Otu00025 8702 MT027180 97%.similar.to.Helicobacter.trogontum.(AY686609).and.H..sencus.(AB006147),.both.enterohepatic.Helicobacter.spp..associated.with.intestinal.diseases
Helico2
Helicobacter.spp. Otu00087 2303 MT027181 99%.identical.to.Helicobacter.aurati.(NR_025124.1),.a.pathogen.of.Syrian.hamsters;.98%.identical.to.H..fennelliae.(GQ867176),.a.human.pathogen
Helico3
Helicobacter.spp. Otu00128 1178 MT027182 99%.Helicobacter.winghamensis.(AF363063),.associated.with.gastroenteritis.in.humans.;.however,.minor.sequences.were.100%.identical.to.H..rodentium.(AY631957).which.is.only.associated.to.gastritis.in.rodents.when.coinfecting.with.other.Helicobacter.strains
Helico4
Neisseria.spp. Otu00612 780 MT027183 97%.uncultured.Neisseria.spp..assoicated.with.human.prostatitis.(HM080767).and.cataracts.(MG696979),.but.undistinguishable.from.environmental.samples.and.healthy.flora.(e.g.,.JF139578)
Neisseria1
Pasteurellaceae Otu00129 1430 MT027184 100%.uncultured.bacterium.(MN095269).of.mouse.oral.flora;.99%.Muribacter.muris.(KP278064).of.unknown.pathogenicity,.water.fowl.pathogen.Avibacterium.gallinarum.(AF487729),.and.cattle.respiratory.disease.agent.Mannheimia.haemolytica.(CP017491)
Pasteurella1
Pasteurellaceae Otu00203 521 MT027185 99%.Aggregatibacter.aphrophilus.(LR134327).and.Haemophilus.parainfluenzae.(CP035368).opportunistic.pathogens.but.otherwise.part.of.normal.flora
Pasteurella2
Rickettsiella.spp. Otu00187 592 MT027186 99%J100%.identity.to.several.endosymbionts.of.insects,.eg..uncultured.Diplorickettsia.spp..in.sand.flies.(KX363696),.Rickettsiella.spp..in.Ixodes.ticks.(KP994859);.99%.identity.to.Rickettsiella.agriotidis.(HQ640943).pathogen.of.wireworms
Rickettsiella
Streptococcus.spp. Otu00115 1681 MT027187 99%.Streptococcus.hyointestinalis.from.intestines.of.swine.(KR819489)
Streptococcus
Yersinia.spp. Otu00041 7420 MT027188 A.heterogeneous.OTU.some.major.sequences.100%.Yersinia.spp..and.Serratia.spp.,.including.pathogenic.zoonotic.bacteria.(e.g.,.Y..pestis.NR_025160).and.nonJpathogenic.endosymbionts.of.plants.(NR_157762).;.some.major.sequences.100%.Pantoea.agglomerans.(MN515098).opportunists
Yersinia
Fusobacterium.spp. Otu00791 108 MT027189 100%.Fusobacterium.ulcerans.(CP028105).from.tropical.foot.ulcers.in.humans;.but.also.100%.identity.with.other.fecal.isolates.of.unknown.pathogenicity.in.mammals.(e.g.,.F..varium.LR134390)
Fusobacterium
Neisseria.spp. Otu00454 148 MT027190 98%.uncultured.microbiota.of.bat.mating.organs.(KY300287),.98%.Simonsiella.muelleri.commensul.from.human.saliva.(AF328145);.97%.Kingella.kingae.(MF073277).pathogen.in.humans
Neisseria2
Treponema.spp. Otu00235 348 MT027191 93%.uncultured.rumen.Treponema.spp..(AB537611) TreponemaWilliamsia.spp. Otu00614 274 MT027192 100%.Williamsia.phyllospherae.(MG205541).and.Williamsia.maris.
(NR_024671),.closely.related.to.opportunistic.pathogens.in.humans
Williamsia
Uncertin(pathogenicity,(not(reliably(detectable
Uncertin(pathogenicity,(reliably(detectable
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36
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